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Author Topic: Nearest Neighbor enhancements  (Read 1850 times)
Full Member
Posts: 160

« on: March 26, 2009, 07:34:59 PM »


I would like to see NearestNeighbor enhanced in the following ways:

1) When computing the nearest neighbor for a given point, optionally exclude points where distance = 0 (most often, this will be just itself).  As it is now, if I build a KNN model on a dataset with K=1, and then apply the model to the dataset, the predictions are perfect since the nearest neighbor of each point is itself.  This is similar to what Weka's LinearNNSearch option -S does (although the other nearest neighbor algorithms Weka supports unfortunately don't have this option).

2) Be able to specify the weighting kernel function, rather than just have a toggle for"weighted_vote".  This would bring it up to the same capabilities as W-LWL, in which can specify linear, Epanechnikov, tricube, inverse, or gaussian weights.

3) Ability to build a full local polynomial regression (aka loess) model, similar to what locfit does in R.

Ingo Mierswa
Hero Member
Posts: 1233

« Reply #1 on: March 31, 2009, 10:56:16 AM »

Hi Keith,

thanks for sending those suggestions in. Some are easier to implement, others will of course need more time. However, I have added all points to our Todo list.


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